Publication:
Automated transtibial prosthesis alignment: A systematic review

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dc.citedby0
dc.contributor.authorKhamis T.en_US
dc.contributor.authorKhamis A.A.en_US
dc.contributor.authorAl Kouzbary M.en_US
dc.contributor.authorAl Kouzbary H.en_US
dc.contributor.authorMokayed H.en_US
dc.contributor.authorRazak N.A.A.en_US
dc.contributor.authorOsman N.A.A.en_US
dc.contributor.authorid57900244500en_US
dc.contributor.authorid57771696300en_US
dc.contributor.authorid57202956887en_US
dc.contributor.authorid57216612501en_US
dc.contributor.authorid35085400100en_US
dc.contributor.authorid42261165400en_US
dc.contributor.authorid57244856600en_US
dc.date.accessioned2025-03-03T07:41:47Z
dc.date.available2025-03-03T07:41:47Z
dc.date.issued2024
dc.description.abstractThis comprehensive systematic review critically analyzes the current progress and challenges in automating transtibial prosthesis alignment. The manual identification of alignment changes in prostheses has been found to lack reliability, necessitating the development of automated processes. Through a rigorous systematic search across major electronic databases, this review includes the highly relevant studies out of an initial pool of 2111 records. The findings highlight the urgent need for automated alignment systems in individuals with transtibial amputation. The selected studies represent cutting-edge research, employing diverse approaches such as advanced machine learning algorithms and innovative alignment tools, to automate the detection and adjustment of prosthesis alignment. Collectively, this review emphasizes the immense potential of automated transtibial prosthesis alignment systems to enhance alignment accuracy and significantly reduce human error. Furthermore, it identifies important limitations in the reviewed studies, serving as a catalyst for future research to address these gaps and explore alternative machine learning algorithms. The insights derived from this systematic review provide valuable guidance for researchers, clinicians, and developers aiming to propel the field of automated transtibial prosthesis alignment forward. ? 2024 Elsevier B.V.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo102966
dc.identifier.doi10.1016/j.artmed.2024.102966
dc.identifier.scopus2-s2.0-85202159952
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85202159952&doi=10.1016%2fj.artmed.2024.102966&partnerID=40&md5=9d1a2857e4591eb0ce36bdfdafa202fc
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/36280
dc.identifier.volume156
dc.publisherElsevier B.V.en_US
dc.sourceScopus
dc.sourcetitleArtificial Intelligence in Medicine
dc.subjectAlgorithms
dc.subjectArtificial Limbs
dc.subjectHumans
dc.subjectMachine Learning
dc.subjectProsthesis Design
dc.subjectProsthesis Fitting
dc.subjectTibia
dc.subject'current
dc.subjectAlignment system
dc.subjectAutomated alignments
dc.subjectAutomated process
dc.subjectBelow-knee prosthesis
dc.subjectMachine learning algorithms
dc.subjectManual identification
dc.subjectProsthetic alignment
dc.subjectSystematic Review
dc.subjectTrans-tibial prosthesis
dc.subjectalgorithm
dc.subjectautomation
dc.subjectbiomechanics
dc.subjecthuman
dc.subjectmachine learning
dc.subjectprosthetic alignment
dc.subjectReview
dc.subjectsystematic review
dc.subjectlimb prosthesis
dc.subjectmachine learning
dc.subjectprocedures
dc.subjectprosthesis design
dc.subjectprosthetic fitting
dc.subjectsurgery
dc.subjecttibia
dc.subjectAdversarial machine learning
dc.titleAutomated transtibial prosthesis alignment: A systematic reviewen_US
dc.typeReviewen_US
dspace.entity.typePublication
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